code: def get(identifier):
      """
      Get the optimizer specified by the identifier.
  
      Parameters
      ----------
      identifier: str or tf.optimizers.Optimizer instance
          Name of the optimizer to use (with default parameters) or instance of the class `tf.optimizers.Optimizer`.
  
      Returns
      -------
      optimizer: OptimizerWrapper
          Instance of `tf.optimizers.Optimizer` wrapped around by `OptimizerWrapper` so that graph partitioning
          is supported.
  
      """
      if isinstance(identifier, tf.optimizers.Optimizer):
          return OptimizerWrapper(identifier)
      elif isinstance(identifier, OptimizerWrapper):
          return identifier
      elif isinstance(identifier, six.string_types):
          optimizer = tf.keras.optimizers.get(identifier)
          return OptimizerWrapper(optimizer)
      else:
          raise ValueError(
              "Could not interpret optimizer identifier:", identifier
          )
  
file_path: AmpliGraph/ampligraph/latent_features/optimizers.py
imports: import abc
  import logging
  import six
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: get(identifier) and returns: OptimizerWrapper, identifier, OptimizerWrapper